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24/06/2026 8:47 pm
TL;DR: AI costs are eroding gross margin for 84% of companies, with 58% reporting a 6 to 15% hit and 26% seeing 16% or more, according to Mavvrik research cited by Kong. The real governance issue is not AI adoption itself but fragmented visibility, attribution, and enforcement across the AI stack.
NHIMG editorial — based on content published by Kong: The Hidden AI Fragmentation Tax: Why AI Innovation Speed Will Depend on Your AI Program Margins
By the numbers:
- 84% of companies report more than a 6% hit to gross margin from AI costs.
- Only 15% of companies can forecast AI costs within ±10% accuracy.
- A full 61% of companies run AI workloads across a combination of public and private environments and different tools.
Questions worth separating out
Q: How should teams govern AI costs across multiple clouds and toolchains?
A: Teams should govern AI costs through one inventory, one attribution model, and one enforcement layer that spans gateways, models, and runtime services.
Q: Why do fragmented AI environments make cost control harder?
A: Fragmented environments make cost control harder because consumption happens across many different systems, each with its own telemetry and policy boundary.
Q: What signals show that AI cost governance is not working?
A: The clearest signals are poor forecast accuracy, incomplete attribution, and delayed discovery of overruns.
Practitioner guidance
- Build a single AI consumption inventory Document every AI touchpoint that can generate cost or policy exposure, including MCP clients, LLMs, APIs, event streams, gateways, and service meshes.
- Tie usage attribution to business ownership Require every measurable AI workload to map to a team, product, or customer so finance and platform teams can explain margin impact without manual reconciliation.
- Unify visibility before enforcing limits Start by consolidating cost, consumption, and runtime telemetry across environments, then apply thresholds and policy guardrails.
What's in the full article
Kong's full blog post covers the operational detail this post intentionally leaves for the source:
- The detailed cost-control architecture Kong proposes for unified AI metering and billing
- The full breakdown of the AI connectivity stack across MCP servers, gateways, event streams, and service meshes
- Kong's explanation of how OpenMeter is used for real-time usage attribution and enforcement
- The vendor's specific implementation context for Konnect Metering and Billing in early access
👉 Read Kong's analysis of the hidden AI fragmentation tax and cost governance →
AI fragmentation tax: what it means for governance teams?
Explore further
25/06/2026 5:36 am
AI fragmentation is becoming a governance debt, not just an architecture inconvenience. The moment AI workloads span multiple clouds, gateways, and execution layers, the organisation inherits a control problem that cannot be solved with isolated reporting tools. Visibility, attribution, and enforcement have to be treated as one discipline, because cost leakage is usually the symptom of governance fragmentation. Practitioners should treat this as a programme design issue, not a tooling detail.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), according to AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, which leaves 48% with a blind spot for compliance and breach investigation.
A question worth separating out:
Q: What is the difference between visibility and enforcement in AI governance?
A: Visibility tells you where AI usage is happening and who or what is consuming resources. Enforcement is the ability to stop, limit, or shape that usage in real time based on policy. Organisations need both, because seeing overruns after the fact does not protect margin or prevent uncontrolled growth.
👉 Read our full editorial: AI fragmentation tax is turning speed into a margin problem
